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This page contains the scripts used for secondary analysis and plotting in "Classification and characterization of microsatellite instability across 18 cancer types" found in Nature Medicine.

Microsatellite instability (MSI), the spontaneous loss or gain of nucleotides from repetitive DNA tracts, is a diagnostic phenotype for gastrointestinal, endometrial, and colorectal tumors, yet the landscape of instability events across a wider variety of cancer types remains poorly understood. To explore MSI across malignancies, we examined 5,930 cancer exomes from 18 cancer types at more than 200,000 microsatellite loci and constructed a genomic classifier for MSI. We identified MSI-positive tumors in 14 of the 18 cancer types. We also identified loci that were more likely to be unstable in particular cancer types, resulting in specific instability signatures that involved cancer-associated genes, suggesting that instability patterns reflect selective pressures and can potentially identify novel cancer drivers. We also observed a correlation between survival outcomes and the overall burden of unstable microsatellites, suggesting that MSI may be a continuous, rather than discrete, phenotype that is informative across cancer types. These analyses offer insight into conserved and cancer-specific properties of MSI and reveal opportunities for improved methods of clinical MSI diagnosis and cancer gene discovery.

Primary exome sequence alignments can be downloaded from the TCGA Research Network. Primary and processed MSI calls derived from these exome alignments, the MOSAIC classifier itself, and intermediate results and tables are available here.

1. Call microsatellite markers in hg18 or hg19 using MISA or download those markers from the UCSC Genome Browser.
2. Implement mSINGS or lobSTR to identify unstable microsatellites throughout the genome from paired somatic and tumor exome BAMs at each microsatellite site.
3. Reformat those outputs into a two or three column format that includes "msi", "peak_avg", and "X8.7679723.7679741"; that is, an ID for the genomic coordinates centered on each microsatellite marker (msi), the average gain in unique alleles in tumor relative to matched normal tissue across all interrogated microsatellites (peak_avg), and a binary variable indicating whether the locus within DEFB105A/B, chr. 8:7679723–7679741 ("X8.7679723.7679741") is unstable.
4. Use the MOSAIC classifier ("mosaic_classifier_063016.robj") to predict those MSI classes based on these data: predict(mosaic, <data>, type="raw")

Perl, Python, and R languages were used for various steps throughout the primary and secondary analyses, including the ggplot2, rpart, clusterProfiler, qvalue, and survival libraries, among others. We also used several external computational tools, such as MISA, mSINGS, and Grid Engine.

Please let Ron Hause [email protected] or Steve Salipante [email protected] know if anything is missing or if you have any questions. Our apologies ahead of time for any and all chaos. :)